UC Davis

The increase in extreme weather conditions and greater penetration of intermittent renewable energy, such as solar and wind,have resulted in electric grid balancing challenges, and greenhouse gas emissions remain high during peak hours. Residentialload flexibility programs help mitigate these challenges by shaping the electricity demand of major appliances to better matchgrid-level renewable electricity generation profiles. This study evaluates the load flexibility of a multi-function heat pump(MFHP) system responding to a dynamic price signal. The residential MFHP uses a single air-source heat pump outdoor unitto efficiently meet both water heating and space conditioning needs. The MFHP does not require electric resistance heatersfor emergency heat or defrost, potentially avoiding the need for electrical panel upgrades commonly required when replacinggas appliances. A 14 kW (48 kBtu/h) MFHP system is installed in an occupied residential building in California. To achieveload flexibility, a rule-based control algorithm adjusts water heating and space cooling setpoints with the objective ofminimizing electric energy cost in response to the dynamic price signal. Model-based control approaches require training dataand forecasting, increasing the complexity and commissioning time. The simplicity of this rule-based control approach will beeasier to adopt for widescale use because it has lower computational complexity, works with equipment from differentmanufacturers, and does not require in-depth programming and commissioning. The dynamic price signal used by thealgorithm consists of a forecast of hourly prices for the next 24-hour period. The rule-based algorithm determines hourlysetpoint schedules for the next 24-hour period based on the dynamic price signal for the water heating and space conditioningand communicates them to the MFHP thermostats.